Projection displays include a highlight projector and a main projector. Highlights projected by the highlight projector boost luminance in highlight areas of a base image projected by the main projector. Various highlight projectors including steerable beams, holographic projectors and spatial light modulators are described.

First interpolated optical flow data is based, at least in part, on an optical flow from a preceding frame, an optical flow from a following frame, or a combination thereof, with a reduced resolution. First interpolated motion vector data based, at least in part, on motion vectors from a preceding frame, a following frame, or a combination thereof, with a reduced resolution. A motion vector nearest in depth is determined from among the first interpolated motion vector data, or an optical flow nearest in depth is determined from among the first interpolated optical flow data, or a combination thereof, for each pixel of an interpolated frame, and are used to selectively gather one or more color signal values for at least some pixels in the interpolated frame from the preceding frame or the following frame, or a combination thereof.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for providing content to user devices. One of the methods includes receiving user generated content from a user device; identifying an image associated with the user; identifying one or more key colors from the image; generating a gradient based on one of the one or more key colors; and generating content for delivery to user devices using the user generated content, the image, and the generated gradient.

Techniques are described for quickly finding a compression mode for BC7 encoding by modelling three sources of error in compression, namely, projection error, endpoint quantization error, and interpolation index quantization error. The mode with the lowest total error is selected for a block to be compressed.

A computing device executing a browser application obtains a dataset for rendering a data visualization, the dataset including a plurality of data points. The device selects, from the plurality of data points, a first subset of data points according to a statistical data distribution of the dataset. The device recursively applies a first algorithm to the first subset of data points to obtain a final subset of data points. Each of first subset of data points and the final subset of data points has a fewer number of data points than the plurality of data points. The device renders a data visualization using the browser application. The data visualization has a plurality of data marks corresponding to the final subset of data points. The device displays, on the browser application, the data visualization including the plurality of data marks.

HDR color patches are sampled throughout an HDR color space parameterized by a parameter. Reference SDR color patches, input HDR color patches and reference HDR color patches are generated from the sampled HDR color patches. An optimization algorithm is executed to generate an optimized forward reshaping mapping and an optimized backward reshaping mapping. The optimized forward reshaping mapping is used to forward reshape input HDR images into forward reshaped SDR images, whereas the optimized backward reshaping mapping is used to backward reshape the forward reshaped SDR images into backward reshaped HDR images.

Example aspects of the present disclosure are directed to systems and methods for learning data augmentation strategies for improved object detection model performance. In particular, example aspects of the present disclosure are directed to iterative reinforcement learning approaches in which, at each of a plurality of iterations, a controller model selects a series of one or more augmentation operations to be applied to training images to generate augmented images. For example, the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image.

An image processing method, an image processing device, an electronic device, and a computer-readable storage medium are provided. The image processing method includes: recognizing and analyzing an original image to obtain theme elements of the original image and facial attribute information of a target object in the original image, and determining text prompt information based on a predetermined target style, the theme elements and the facial attribute information; performing pose estimation on the original image to obtain pose information of the target object in the original image; performing noise adding processing on the original image to obtain a target noise image; generating image noise based on the target noise image, the text prompt information, the facial attribute information, and the pose information; and generating a target image of the predetermined target style based on the image noise and the target noise image.

A method includes: providing color images for a geological rock core sample, creating initial masks for a subset of the color images, dividing each color image into sets of image tiles, splitting the sets of image tiles into a training set and a validation set, augmenting the training and validation sets, including orienting each image tile in a same direction by sample depth, training a model with the augmented training validation sets, generating image masks from the trained model, corresponding to the color images, combining the sets of image tiles to regenerate each of the color images from their image tile sets, applying the generated image masks to the color images to generate greyscale masked images, stacking the greyscale masked images by sample depth, and applying colors to the stacked greyscale masked images to generate and display a stacked color image representing the entire sample length.

The present disclosure relates to stain unmixing of digital pathology images by determining initial color vectors associated with digital pathology stains (or chromogens) from pure-color digital pathology images. The determined color vectors may be fine-tuned or adjusted to help improve the stain unmixing performance. The adjustment may be performed via the interface and/or automated technique that, based on a real multiplex image and one or more synthetic singleplex images, perform adjustments to the color vectors. These adjusted color vectors may be further leveraged for stain unmixing of a given multiplex image. Additionally, the disclosure provides techniques to generate synthetic pixels and the associated color vectors, a recommended stain to be added to a multiplex image and/or generation of multiplex images from one or more digital pathology images based on the targeted color vectors.